AI anywhere is taking hold as companies need to quickly evaluate data to influence smarter business decisions and remain competitive.
While artificial intelligence (AI) has been around for decades, it was only used for very limited applications. All of that is changing as companies need to quickly evaluate data to influence smarter business decisions and remain competitive. To accomplish this, they need AI embedded in their business processes.
So-called AI anywhere is a favorite topic of Rob Thomas, General Manager, IBM Data and AI. In his keynote at last week’s Data and AI Forum 2019 in Miami, he noted that while AI represents “the largest economic opportunity of our lifetime (estimated to contribute $16 trillion to GDP by 2030),” enterprise adoption is low.
See also: IBM Doubles Down on its Watson AI Bet
This reason for this paradox (great opportunity, but limited adoption) is due to several reasons, including lack of skills, lack of tools, and lack of confidence. But the biggest issue is cultural. His suggestion:
“For organizations that want to participate in this phase of innovation and wealth creation in technology, the most important thing is a beginner’s mindset, a willingness to try, and an acceptance of failure. Organizations should seek to do 100 AI experiments a year, knowing that more than 50% will fail. Many company cultures are not suited for that. A more typical approach is to rally around one big AI project, committing a lot of people, time, and money. I do not advise that approach. AI is about mass experimentation, not one big project implementation.”
As companies adopt such strategies, he expects AI anywhere to become the norm. “I believe every human being on Earth will interact with Watson in some way – whether it’s accelerating the customer service they receive, augmenting the work they do, improving their retail experiences, providing medical insights to their caregivers, helping them to avoid food scarcity or even ways that have not been conceived yet.”
Overcoming AI Adoption Obstacles
Thomas believes there are three areas (or beachheads) where companies need help when implementing AI anywhere. They include platform, skills, and research. IBM helps in these areas in several ways, including:
- A differentiated platform that offers ease of AI customization, automation of AI, expandability of AI models, and embedded capabilities
- By addressing the skills deficit through automation and IBM’s expertise
- Continued research, as evident by IBM’s 93% year-to-year growth in research conference papers published. There is an increasing impact of its investment in the MIT-IBM Watson AI Lab, as represented by citations. And, IBM is in the top 10 of organizations with patents in supervised and unsupervised learning.
From a practical perspective for companies wishing to move to an AI anywhere strategy, IBM offers Watson. Watson is software capable of making sense of data sets and understanding natural language to provide recommendations, make predictions, and automate work.
The tools for Watson include software that can be used for free or purchased/subscribed to when a company wants to use it more broadly. These tools help an organization collect data, organize data, build AI models, put AI models into production, and manage those models over time. Here’s a little-known fact: 85% of the work that happens inside these Watson tools is open source (Python, R, TensorFlow, etc.). After all, open-source is the lingua franca of data and AI. Watson exists in three forms:
- Tools for companies that want to build their own AI
- Applications for companies that want to buy a pre-packaged AI solution
- Embedded machine learning and AI features
The applications of Watson are prepackaged software that we built to solve a specific business problem. The leading application is Watson Assistant, for augmenting customer service agents. The Royal Bank of Scotland (RBS) is a great example of a company leveraging Watson Assistant. Using Watson Assistant, they are now automating 40% of their inbound customer inquiries. With that automation, they shifted their customer service representatives to the hardest problems, increasing their customer satisfaction.
Other applications include Watson Discovery, Planning Analytics with Watson, RegTech with Watson, and Maximo with Watson.
Ultimately, Watson – whether it’s the tools or the applications – can help companies save vast amounts of human hours and millions of dollars every year. And in other cases, it’s driving significant revenue increases.
